Long-Term Effects of Multimedia Education and “Foot Alerts” Through the MyU App on Diabetic Foot Care in Individuals With Moderate-High Risk: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background: Diabetic foot ulceration (DFU) is the leading cause of nontraumatic amputations in people with diabetes. Research shows that improving patient awareness can result in short-term improvements, but Cochrane reviews report insufficient high-quality evidence. Objective: This study aims to investigate the effects of multimedia presentation and smartphone alerts to enhance long-term knowledge and foot care behaviors in individuals at moderate-to-high risk of DFU. Methods: Participants were randomized to a control group, receiving usual diabetic foot care advice (n=40), or an intervention group, receiving a multimedia diabetic foot care presentation and regular "foot alerts" through the MyU smartphone app on top of usual care (n=37). Patient's knowledge and behaviors related to diabetic foot care were assessed at baseline and after 12 months. Repeated measures ANOVA was conducted in both intention-to-treat and per-protocol analyses to evaluate the intervention's effectiveness. Results: The findings were consistent across intention-to-treat and per-protocol analyses. In the intervention group, the number of podiatry visits was positively correlated with improved foot care behavior (r=0.408; P=.02), while the control group showed a negative correlation (r=-0.402; P=.02). No significant correlations were observed with knowledge scores. Although no significant time×group interactions were seen, the main effects of time were found for both knowledge (η²=0.12; P=.004) and behavior scores (η²=0.31; P<.001). Post hoc analysis showed a decline in knowledge scores in the control group (Cohen d=-0.24; P=.007) and improvements in behavior scores in both groups (Cohen d: intervention=0.61, control=0.63; all P<.001). Conclusions: The MyU app-based multimedia intervention was associated with improved foot care behaviors over 12 months, indicating potential benefits as an adjunct to usual diabetic foot care. However, no significant changes in diabetic foot care knowledge were observed. These findings suggest that while the applied digital multimedia tool may support behavior change, further research is needed to enhance knowledge retention and clinical impact. The study revealed that multimedia education alone may not be effective for long-term improvement in foot self-care knowledge and behavior among individuals at moderate-high DFU risk, but the reinforcement of educational material during follow-up podiatry visits could be effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".